Lambdas & Functional Tools

Introduction

Lambdas are anonymous one-expression functions. Combined with map, filter, and sorted(key=...), they enable concise functional pipelines.

Understanding the topic

Core concepts to understand:

• lambda x: x * 2 — single expression only.
• Common with sorted, filter, max.
• Prefer comprehensions over map/filter.

Syntax reference

Visual flow / code:

square = lambda x: x * x
print(square(5))  # 25

# Sort by key
people = [{"name": "B", "age": 30}, {"name": "A", "age": 25}]
people.sort(key=lambda p: p["age"])

# Filter + map
nums = [1, 2, 3, 4]
evens = list(filter(lambda x: x % 2 == 0, nums))
doubled = list(map(lambda x: x * 2, nums))

# Pythonic alternative
evens = [x for x in nums if x % 2 == 0]

Execution workflow

Real-world use

Lambdas shine inside sorted, min, max, and Pandas apply calls — keep them tiny.

Best practices

• Use lambdas only for one-liners.
• Prefer named functions for clarity.
• Comprehensions usually beat map/filter.

Hands-on exercise

Interview preparation — practice these questions:

• Lambda vs def?
• Why prefer comprehensions?
• What's the limit of a lambda?

Summary

In summary: Lambdas = inline one-expression fns. Comprehensions are more Pythonic.